Package: binaryRL 0.9.11
binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks
Tools for building Rescorla-Wagner Models for Two-Alternative Forced Choice tasks, commonly employed in psychological research. Most concepts and ideas within this R package are referenced from Sutton and Barto (2018) <ISBN:9780262039246>. The package allows for the intuitive definition of RL models using simple if-else statements and three basic models built into this R package are referenced from Niv et al. (2012) <doi:10.1523/JNEUROSCI.5498-10.2012>. Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) <doi:10.7554/eLife.49547>. Example datasets included with the package are sourced from the work of Mason et al. (2024) <doi:10.3758/s13423-023-02415-x>.
Authors:
binaryRL_0.9.11.tar.gz
binaryRL_0.9.11.zip(r-4.7)binaryRL_0.9.11.zip(r-4.6)binaryRL_0.9.11.zip(r-4.5)
binaryRL_0.9.11.tgz(r-4.6-x86_64)binaryRL_0.9.11.tgz(r-4.6-arm64)binaryRL_0.9.11.tgz(r-4.5-x86_64)binaryRL_0.9.11.tgz(r-4.5-arm64)
binaryRL_0.9.11.tar.gz(r-4.7-arm64)binaryRL_0.9.11.tar.gz(r-4.7-x86_64)binaryRL_0.9.11.tar.gz(r-4.6-arm64)binaryRL_0.9.11.tar.gz(r-4.6-x86_64)
binaryRL_0.9.11.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
binaryRL/json (API)
| # Install 'binaryRL' in R: |
| install.packages('binaryRL', repos = c('https://yuki-961004.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yuki-961004/binaryrl/issues
Pkgdown/docs site:https://yuki-961004.github.io
- Mason_2024_G1 - Group 1 from Mason et al.
- Mason_2024_G2 - Group 2 from Mason et al.
mapmlepsychologyrcppreinforcement-learningtafccpp
Last updated from:6078e06742. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 124 | ||
| linux-devel-x86_64 | OK | 129 | ||
| source / vignettes | OK | 227 | ||
| linux-release-arm64 | OK | 133 | ||
| linux-release-x86_64 | OK | 116 | ||
| macos-release-arm64 | OK | 138 | ||
| macos-release-x86_64 | OK | 142 | ||
| macos-oldrel-arm64 | OK | 115 | ||
| macos-oldrel-x86_64 | OK | 210 | ||
| windows-devel | OK | 98 | ||
| windows-release | OK | 119 | ||
| windows-oldrel | OK | 123 | ||
| wasm-release | OK | 115 |
Exports:fit_pfunc_epsilonfunc_etafunc_gammafunc_loglfunc_pifunc_tauoptimize_pararcv_drecovery_datarpl_eRSTDrun_msimulate_listTDUtility
Dependencies:codetoolsdigestdoFuturedoRNGforeachfuturefuture.applyglobalsiteratorslistenvparallellyprogressrRcpprngtools
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Step 3: Optimizing parameters to fit real data | fit_p |
| Function: Epsilon Related | func_epsilon |
| Function: Learning Rate | func_eta |
| Function: Utility Function | func_gamma |
| Function: Loss Function | func_logl |
| Function: Upper-Confidence-Bound | func_pi |
| Function: Soft-Max Function | func_tau |
| Group 1 from Mason et al. (2024) | Mason_2024_G1 |
| Group 2 from Mason et al. (2024) | Mason_2024_G2 |
| Process: Optimizing Parameters | optimize_para |
| Step 2: Generating fake data for parameter and model recovery | rcv_d |
| Process: Recovering Fake Data | recovery_data |
| Step 4: Replaying the experiment with optimal parameters | rpl_e |
| Model: RSTD | RSTD |
| Step 1: Building reinforcement learning model | run_m |
| Process: Simulating Fake Data | simulate_list |
| S3method summary | summary.binaryRL |
| Model: TD | TD |
| Model: Utility | Utility |
